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The World Health Organization reports that heart disease is the most common cause of death globally, accounting for 17.9 million fatalities annually. The fundamentals of a cure, it is thought, are important symptoms and recognition of the illness. Traditional techniques are facing many challenges, ranging from delayed or unnecessary treatment to incorrect diagnoses, which can affect treatment progress, increase the bill, and give the disease more time to spread and harm the patient’s body. Such errors could be avoided and minimized by employing ML and AI techniques. Many significant efforts have been made in recent years to increase computer-aided diagnosis and detection applications, which is a rapidly growing area of research. Machine learning algorithms are especially important in CAD, which is used to detect patterns in medical data sources and make nontrivial predictions to assist doctors and clinicians in making timely decisions. This study aims to develop multiple methods for machine learning using the UCI set of data based on individuals’ medical attributes to aid in the early detection of cardiovascular disease. Various machine learning techniques are used to evaluate and review the results of the UCI machine learning heart disease dataset. The proposed algorithms had the highest accuracy, with the random forest classifier achieving 96.72% and the extreme gradient boost achieving 95.08%. This will assist the doctor in taking appropriate actions. The proposed technology will only be able to determine whether or not a person has a heart issue. The severity of heart disease cannot be determined using this method.

During the pandemic, it has been seen that the global population follows the guidelines issued by the health organization regarding wearing face masks, but some people do not take care of this and do not use masks. The objective of the proposed system, Wollega University Face Mask Detection System (WUFMDS), is to restrict people who are not wearing a mask on the door side by identifying the face mask from the face or open the door if the incoming person is wearing the mask. This system is based on the Internet of Things (IoT) and a Deep Learning algorithm called Convolutional Neural Network (CNN). For this purpose, images with and without masks were collected as samples from the university. The CNN algorithm is used to detect the mask and classify it as with or without masks. The IoT module controls the door operation based on the classification response sent to the IoT module by the CNN algorithm. The system was tested lively with the dummy door system in order to ensure the functionality of the face mask detection system and developed software applications for the system model are working as defined objectives. Our model had 99.36% accuracy with the training dataset and 99.29% accuracy with the validation set. Hence, the proposed system could be used for the automatic identification and classification of masks on the face and to operate the door to allow the person who is wearing the mask to pass through while keeping it closed when no mask is found on the face.

A peer-to-peer (P2P) decentralized information-sharing network is used to share data and maintain security, privacy, and integrity standards called blockchain. In this case, information sharing and updating require regular simplification. The presented systematic review mainly focuses on the interoperability of electronic health records (EHRs) using blockchain. Correspondingly, 18 blockchain-based solutions were selected to address the interoperability challenges of EHRs. The limitation of solutions includes reliability, privacy, integrity, sharing, and standards. This systematic review contains six phase’s research question, research phase, article selection, abstract-based keyword, data extraction, and progress tracking. Various Web resources such as Google Scholar, Web of Science, and IEEE are used to extract the relevant manuscripts. Primarily, 18 articles were selected to present the interoperable requirements of EHRs using blockchain, standards of blockchain-based EHRs, and solutions for interoperability of EHRs using blockchain. The conducted study explains the best available interoperable blockchain-based EHR standards, implementations, applications, and challenges.

In this work, we propose and encourage the sending of an IoT-based domestic systems conditions small scale programmable framework for grid-tie matrix converter that can efficiently be executed with low-control equipment plan with quick execution capacity. The proposed framework for matrix grid-tie converter has been performed based on an adaptive fuzzy sliding technique and furthermore monitors the grid status through an IoT server. The adaptive fuzzy sliding method was connected to the framework, and a unity power factor was fulfilled alongside the wanted sinusoidal input and output results. The proposed AFS-based matrix converter has been validated through simulation using MATLAB software. The control clock signals are generated for the positive and negative clock pulses of the sequence. Here the sinusoidal reference signal is compared with the switching frequency of the triangular carrier frequency. The hardware results were also verified to validate the simulation. When diverged from other standard techniques, the proposed AFS technique has achieved 97.23% efficiency in full load conditions.

Machine learning and data analytics are two of the most popular subdisciplines of modern computer science which have a variety of scopes in most of the industries ranging from hospitals to hotels, manufacturing to pharmaceuticals, mining to banking, etc. Additionally, mining and hospitals are two of the most critical industries where applications when deployed security, accuracy, and cost effectiveness are the major concerns, due to the huge involvement of man and machines. In this paper, the problem of finding out the location of man and machines has been focused on in case of an accident during the mining process. The primary scope of the research is to guarantee that the projected position is near to the real place so that the trained model’s performance can be tested. The solution has been implemented by first proposing the MLAELD (Machine Learning Architecture for Excavators’ Location Detection), in which Bluetooth Low Energy (BLE) beacons have been used for tracking the live locations of excavators preceded by collecting the data of the signal strength mapping from multiple beacons at each specific point in a closed area. Second, machine learning techniques are proposed to develop and train multioutput regression models using linear regression, K-nearest neighbor regression, decision tree regression, and random forest regression. These techniques can predict the live locations of the required persons and machines with a high level of precision from the last beacon strengths received.

Artificial intelligence (AI) is a machine intelligence technology with vast potential for a smart industrial revolution. This study aims to create new electric-powered transportation such as bicycles, motorbikes, and other vehicles. The combination of high-speed, resiliency, and low-latency connection, as well as AI and Internet of Things (IoT) technologies, will enable the transformation to fully autonomous vehicles. This electric motorcycle tracks its speed, location, and other data using IoT logic. A smart charging system has also been developed to keep them ready to use effectively. The proposed research also reduces the damage to the environment efficiently.

Application of internet of things has been frequently used in smart cities. It has been observed that people at different localities are showing interest in IoT applications for smart cities. Their interest, awareness, and significant consideration for IoT application varies according to their qualification, locality, and standard of living. But it is essential to know the interest; the awareness of public regarding such systems. Present research is focused of IoT automation in case of smart cities and considered IoT applications for commercial and healthcare. Present work is considering awareness, interest, and significance of consideration among males and females. Research is focusing whether there is awareness in people in IoT automation for healthcare and commercial applications. The interest of people for an IoT automation system of a smart city is also taken in account.

Many innovations are made mainly for the individual with a disability. The primary aim of the effort is to solve the issue faced by the world's dumb people. It's application-specific, where we solve a dumb medical attendant's issues. The project's main aim is to develop an instrument to answer consumer inquiries in the medical store, which works as an electronic voice device. A Raspberry PI 3 with a keyboard and speaker are suggested. In the Raspberry Pi, a text-to-speech coder is used. The stupid person needs to input the response through a keyboard, depending on the client query. The value is transformed in audio output by pressing the key equivalent. The response time is 2.6 seconds.

The new technology of computer-oriented services is cloud computing. Cloud computing is becoming increasingly common and continues to expand due to rapid growth in the “cloud computing” sector. It makes major safety issues in big organizations more cost-effective since they pool valuable resources. As a result of the growing demand for more clouds and data, many security problems such as confidentiality, integrity and authentication could emerge in an open setting. These innovations have transformed the shape of conventional IT. This technology provides numerous advantages to IT companies, despite being challenged to meet their maturity. This paper has been debated Data security and privacy concerns are critical for both cloud architecture hardware and applications. The algorithm preserves confidential data across the cloud through encryption and decryption. This algorithm may be used for improving data confidentiality in the results assessment.

A prospective study was carried out in 100 cases of suspected occult spinal dysraphic anomalies with Magnetic Resonance Imaging in order to determine its diagnostic efficacy as the initial imaging modality. MR imaging provided accurate preoperative information in 91 out of 92 cases (98.9%). Some of the unusual and interesting findings in the series were: presence of intrinsic cord abnormality in 19 out of 21 cases (90.4%) with a normal plain radiograph, 4 cases of diastematomyelia with a dermoid in the dorsal and lumbar region associated with syringohydromyelia, intradural fibrous/glial bands, sgringo-hydromyelia/myelomalacia of the conus with tethered cord syndrome having a normally placed conus, and myelocystocele. It is concluded that MRI is an excellent primary diagnostic tool, together with a plain radiograph, for complete preoperative evaluation of mid-tine spinal anomalies.

Eight patients with neurocysticercosis treated with praziquantel (PZQ) were studied with serial MR imaging to demonstrate the early effects of the drug in brain lesions. Increase in cyst size and appearance of pericystic edema were observed as early as 24 hr, followed by gradual degeneration of the scolex and cyst contents. Cysts showed varied degree of response to the drug and in their rate of disappearance. MRI was found to be a sensitive technique to objectively document early changes in the parasite and patterns of response to the cysticidal drug in brain cysticercosis.

Project management has been firmly established as a concept for organizing, innovative as well as strategic endeavors. Project managers play a key role in successful project completion. It’s not novel that same project classified as a ‘simple’ by one project manager may be classified as a ‘complex’ by others and there are endless reasons. Soft Computing approaches come into picture in such situation. Now a days, Software engineers emphasis on data repository to extract information in order to better manage projects and to produce higher quality software systems that can be delivered on time and on budget. Improper judgment of project complexity, which results into ‘delayed project’, will occur because of improper estimation of complexity of individual modules of the project. In this paper, each activity diagram of the project is classified into one of the three classes: Simple, average or Complex. Proposed approach is divided into various phases. Pre- Processing phase removes irrelevant information, Feature- Extraction phase extracts various features, Relevance Analysis phase removes irrelevant features. Relevant features and output variable will be fuzzified and used as input to Fuzzy Inference System during the Fuzzy Classification phase.

Hand written character recognition or text recognition is the ability of the system to identify character or text automatically. Documents can be in any form such as documents, photographs, touch-screens or other devices. Each character will have its own feature sets. The classification or identification of characters are done based on proper selection of feature sets. Feature selection is the major step for any classification process. The machine learning model is created based on feature sets. Logistic regression model is used in this model to identify different characters. 97.33% accuracy is achieved using logistic regression model compared to existing work.